Stock market prediction
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock price movements are governed by the random walk hypothesis and thus are inherently unpredictable. Others disagree and those with this viewpoint possess a myriad of methods and technologies which purportedly allow them to gain future price information. The random walk hypothesis When applied to a particular financial instrument, the random walk hypothesis states that the price of this instrument is governed by a random walk and hence is unpredictable. If the random walk hypothesis is false then there will exist some (potentially non-linear) correlation between the instrument price and some other indicator(s) such as trading volume or the previous day's instrument closing price. If this correlation can be determined then a potential profit can be made. Internet based data sources for stock market prediction Google Search Volume Tobias Preis et al. introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends. Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports, suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets. Wikipedia Article Views In a study published in Scientific Reports in 2013, Helen Susannah Moat, Tobias Preis and colleagues demonstrated a link between changes in the number of views of Wikipedia articles relating to financial topics and subsequent large stock market moves. Prediction methods Prediction methodologies fall into three broad categories which can (and often do) overlap. They are fundamental analysis, technical analysis (charting) and technological methods. Fundamental analysis Fundamental Analysts are concerned with the company that underlies the stock itself. They evaluate a company's past performance as well as the credibility of its accounts. Many performance ratios are created that aid the fundamental analyst with assessing the validity of a stock, such as the P/E ratio. Warren Buffett is perhaps the most famous of all Fundamental Analysts. Fundamental analysis is built on the belief that human society needs capital to make progress and if a company operates well, it should be rewarded with additional capital and result in a surge in stock price. Fundamental analysis is widely used by fund managers as it is the most reasonable, objective and made from publicly available information like financial statement analysis. Another meaning of fundamental analysis is beyond bottom-up company analysis, it refers to top-down analysis from first analyzing the global economy, followed by country analysis and then sector analysis, and finally the company level analysis. Technical analysis Technical analysts or chartists are not concerned with any of the company's fundamentals. They seek to determine the future price of a stock based solely on the (potential) trends of the past price (a form of time series analysis). Numerous patterns are employed such as the head and shoulders or cup and saucer. Alongside the patterns, statistical techniques are used such as the exponential moving average (EMA). Candle stick patterns are believed to be first developed by Japanese rice merchants, and nowadays widely used by technical analysts. Alternative methods With the advent of the digital computer, stock market prediction has since moved into the technological realm. The most prominent technique involves the use of artificial neural networks (ANNs) and Genetic Algorithms. ANNs can be thought of as mathematical function approximators. The use of ANN simulates how human brain functions, by feeding computers with massive data to mimic human thinking. The most common form of ANN in use for stock market prediction is the feed forward network utilising the backward propagation of errors algorithm to update the network weights. These networks are commonly referred to as Backpropagation networks. Another form of ANN that is more appropriate for stock prediction is the time recurrent neural network (TRN) or time delay neural network (TDNN). Examples of TRN and TDNN are the Elman, Jordan, and Elman-Jordan networks. (See Elman networks and Jordan networks). For stock prediction with ANNs, there are usually two approaches taken for forecasting different time horizons: independent and joint. The independent approach employs a single ANN for each time horizon, for example, 1-day, 2-day, or 5-day. The advantage of this approach is that network forecasting error for one horizon won't impact the error for another horizon—since each time horizon is typically a unique problem. The joint approach, however, incorporates multiple time horizons together so that they are determined simultaneously. In this approach, forecasting error for one time horizon may share its error with that of another horizon, which can decrease performance. There are also more parameters required for a joint model, which increases the risk of overfitting. Of late, the majority of academic research groups studying ANNs for stock forecasting seem to be using an ensemble of independent ANNs methods more frequently, with greater success. An ensemble of ANNs would use low price and time lags to predict future lows, while another network would use lagged highs to predict future highs. The predicted low and high predictions are then used to form stop prices for buying or selling. Outputs from the individual "low" and "high" networks can also be input into a final network that would also incorporate volume, intermarket data or statistical summaries of prices, leading to a final ensemble output that would trigger buying, selling, or market directional change. A major finding with ANNs and stock prediction is that a classification approach (vs. function approximation) using outputs in the form of buy(y=+1) and sell(y=-1) results in better predictive reliability than a quantitative output such as low or high price.Thawornwong, S, Enke, D. Forecasting Stock Returns with Artificial Neural Networks, Chap. 3. In: Neural Networks in Business Forecasting, Editor: Zhang, G.P. IRM Press, 2004. This is explained by the fact that an ANN can predict class better than a quantitative value as in function approximation—since ANNs occasionally learn more about the noise in the input data. Since NNs require training and have can have a large parameter space, it is useful to modify the network structure for optimal predictive ability. Notes References *Graham, B. The Intelligent Investor HarperCollins; Rev Ed edition, 2003. *Lo, A.W. and Mackinlay, A.C. A Non-Random Walk Down Wall Street 5th Ed. Princeton University Press, 2002. *Azoff, E.M. Neural Network Time Series Forecasting of Financial Markets John Wiley and Sons Ltd, 1994. Category:Finance Category:Economic forecasting Category:Stock market